EdgeFaaS: A Function-based Framework for Edge Computing

The rapid growth of data generated from Internet of Things (IoTs) such as smart phones and smart home devices presents new challenges to cloud computing in transferring, storing, and processing the data. With increasingly more powerful edge devices, edge computing, on the other hand, has the potential to better responsiveness, privacy, and cost efficiency. However, resources across the cloud and edge are highly distributed and highly diverse. To address these challenges, this paper proposes EdgeFaaS, a Function-as-a-Service (FaaS) based computing framework that supports the flexible, convenient, and optimized use of distributed and heterogeneous resources across IoT, edge, and cloud systems. EdgeFaaS allows cluster resources and individual devices to be managed under the same framework and provide computational and storage resources for functions. It provides virtual function and virtual storage interfaces for consistent function management and storage management across heterogeneous compute and storage resources. It automatically optimizes the scheduling of functions and placement of data according to their performance and privacy requirements. EdgeFaaS is evaluated based on two edge workflows: video analytics workflow and federated learning workflow, both of which are representative edge applications and involve large amounts of input data generated from edge devices

Regularized Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity

This paper focuses on reinforcement learning for the regularized robust Markov decision process (MDP) problem, an extension of the robust MDP framework. We first introduce the risk-sensitive MDP and establish the equivalence between risk-sensitive MDP and regularized robust MDP. This equivalence offers an alternative perspective for addressing the regularized RMDP and enables the design of efficient learning algorithms. Given this equivalence, we further derive the policy gradient theorem for the regularized robust MDP problem and prove the global convergence of the exact policy gradient method under the tabular setting with direct parameterization. We also propose a sample-based offline learning algorithm, namely the robust fitted-Z iteration (RFZI), for a specific regularized robust MDP problem with a KL-divergence regularization term and analyze the sample complexity of the algorithm. Our results are also supported by numerical simulations

When Edge Meets FaaS: Opportunities and Challenges

The proliferation of edge devices and the rapid growth of IoT data have called forth the edge computing paradigm. Function-as-a-service (FaaS) is a promising computing paradigm to realize edge computing. This paper explores the feasibility and advantages of FaaS-based edge computing. It also studies the research challenges that should be addressed in the design of such systems, which are 1) the quick decomposing and recomposing of applications, 2) the trade-off between performance and isolation of sandbox mechanisms, and 3) distributed scheduling. The challenges are illustrated by evaluating existing FaaS-based edge platforms, AWS IoT Greengrass, and OpenFaaS

On the Relationship of Optimal State Feedback and Disturbance Response Controllers

This paper studies the relationship between state feedback policies and disturbance response policies for the standard Linear Quadratic Regulator (LQR). For open-loop stable plants, we establish a simple relationship between the optimal state feedback controller $u_t=K_\star x_t$ and the optimal disturbance response controller $u_t=L^{(H)}_{\star;1}w_{t-1}+\cdots+L^{(H)}_{\star;H}w_{t-H}$ with $H$-order. Here $x_t, w_t, u_t$ stands for the state, disturbance, control action of the system, respectively. Our result shows that $L_{\star,1}^{(H)}$ is a good approximation of $K_\star$ and the approximation error $\|K_\star - L_{\star,1}^{(H)}\|$ decays exponentially with $H$. We further extend this result to LQR for open-loop unstable systems, when a pre-stabilizing controller $K_0$ is available

DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Indoor Semantic Segmentation

Deep learning approaches achieve prominent success in 3D semantic segmentation. However, collecting densely annotated real-world 3D datasets is extremely time-consuming and expensive. Training models on synthetic data and generalizing on real-world scenarios becomes an appealing alternative, but unfortunately suffers from notorious domain shifts. In this work, we propose a Data-Oriented Domain Adaptation (DODA) framework to mitigate pattern and context gaps caused by different sensing mechanisms and layout placements across domains. Our DODA encompasses virtual scan simulation to imitate real-world point cloud patterns and tail-aware cuboid mixing to alleviate the interior context gap with a cuboid-based intermediate domain. The first unsupervised sim-to-real adaptation benchmark on 3D indoor semantic segmentation is also built on 3D-FRONT, ScanNet and S3DIS along with 7 popular Unsupervised Domain Adaptation (UDA) methods. Our DODA surpasses existing UDA approaches by over 13% on both 3D-FRONT $\rightarrow$ ScanNet and 3D-FRONT $\rightarrow$ S3DIS. Code will be available

Spin triplet superconducting pairing in doped MoS2_2

Searching for triplet superconductivity has been pursued intensively in a broad field of material science and quantum information for decades. Nevertheless, these novel states remain rare. Within a simplified effective three-orbital model, we reveal a spin triplet pairing in doped MoS$_2$ by employing both the finite temperature determinant quantum Monte Carlo approach and the ground state constrained-phase quantum Monte Carlo method. In a wide filling region of \avg{n}=0.60-0.80 around charge neutrality \avg{n}=2/3, the $f$-wave pairing dominates over other symmetries. The pairing susceptibility strongly increases as the on-site Coulomb interaction increases, and it is insensitive to spin-orbit coupling.Comment: Accepted for publication as a Regular Article in Physical Review

White-box Compiler Fuzzing Empowered by Large Language Models

Compiler correctness is crucial, as miscompilation falsifying the program behaviors can lead to serious consequences. In the literature, fuzzing has been extensively studied to uncover compiler defects. However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates tests without sufficient understanding of internal compiler behaviors. As such, they often fail to construct programs to exercise conditions of intricate optimizations. Meanwhile, traditional white-box techniques are computationally inapplicable to the giant codebase of compilers. Recent advances demonstrate that Large Language Models (LLMs) excel in code generation/understanding tasks and have achieved state-of-the-art performance in black-box fuzzing. Nonetheless, prompting LLMs with compiler source-code information remains a missing piece of research in compiler testing. To this end, we propose WhiteFox, the first white-box compiler fuzzer using LLMs with source-code information to test compiler optimization. WhiteFox adopts a dual-model framework: (i) an analysis LLM examines the low-level optimization source code and produces requirements on the high-level test programs that can trigger the optimization; (ii) a generation LLM produces test programs based on the summarized requirements. Additionally, optimization-triggering tests are used as feedback to further enhance the test generation on the fly. Our evaluation on four popular compilers shows that WhiteFox can generate high-quality tests to exercise deep optimizations requiring intricate conditions, practicing up to 80 more optimizations than state-of-the-art fuzzers. To date, WhiteFox has found in total 96 bugs, with 80 confirmed as previously unknown and 51 already fixed. Beyond compiler testing, WhiteFox can also be adapted for white-box fuzzing of other complex, real-world software systems in general

AN IN SITU PRESERVED EARLY CARBONIFEROUS (SERPUKHOVIAN) BRACHIOPOD COMMUNITY IN SOUTHERN GUIZHOU, CHINA

A brachiopod shell bed from the Lower Carboniferous (Serpukhovian) in Guizhou Province of southern China is reported as representing an in situ preserved brachiopod community. The community is characterized by yielding more than 80% complete and articulated specimens preserved in life position and poor size sorting. A new spiriferide species, Weiningia ziyunensis n. sp., is described in the community, which contains the other eight species belonging to six genera. Morphology and preservation analysis of Weiningia ziyunensis n. sp. suggests that it lived in dense clusters attached to living and dead shells and stabilizing its position with thickened posterior shell. Size-frequency distribution and survivorship curve are applied to the population dynamics investigation of this species. Large number of juvenile shells accompanied by high mortality reflects that many juvenile individuals suffered from the limited life space and turbid environment generated by dense clusters. The same high adult mortality is the result of more pressure from neighbors that lead to shell malformation and eventually low feeding and cleaning efficiency, whereas the low senior mortality is attributed to their abilities to cope with these problems. Members of the community show great difference in numeric frequency, with Weiningia ziyunensis n. sp. being one of the dominant species that was characterized by crowded life strategy. By living in dense clusters on Striatifera striata (Fischer de Waldheim, 1837) or other shell fragments, Weiningia ziyunensis n. sp. could resist the water current and gradually expand its population